Variable Gain Kalman Filter for Battery Temperature Compensation
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Solution Overview
Problem
Existing methods for estimating battery parameters in vehicles, such as state of charge (SOC) and power capability, are not accurate at varying temperatures due to the use of fixed gain factors in extended Kalman filters, leading to inaccuracies in battery management systems.
Innovation Solution
A vehicle system that employs a variable gain scheduling approach in extended Kalman filters based on battery temperature, allowing for more accurate estimation of SOC and power capability by selecting a gain factor from predetermined data, thereby compensating for temperature variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed gain factor is used in extended Kalman filters for battery parameter estimation, then the system complexity is reduced, but the measurement precision deteriorates at varying temperatures
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed gain factor to a variable gain factor in the extended Kalman filter that adapts to changing battery temperatures. The gain factor is dynamically adjusted based on temperature conditions, allowing the system to maintain optimal estimation accuracy across varying thermal environments without requiring complex reconfiguration of the entire system architecture.
Solution Approach 2:
The patent implements parameter changes by modifying the gain factor parameter of the extended Kalman filter based on battery temperature. Different gain factors are selected from predetermined data according to temperature ranges, enabling the system to adapt its estimation characteristics to match the battery's thermal state and maintain measurement precision without increasing overall system complexity.
2Ease of operation
If a fixed gain factor is used in extended Kalman filters, then the ease of operation is improved, but the reliability deteriorates under varying temperature conditions
Solution Approach 1:
The patent changes the gain factor parameter based on battery temperature to maintain reliability across different operating conditions. By selecting from predetermined gain factors corresponding to different temperature ranges, the system adapts its behavior to ensure accurate battery parameter estimation without complicating the operational interface or control logic.
Solution Approach 2:
The patent incorporates feedback by using battery temperature information to adjust the gain factor selection in the extended Kalman filter. This feedback mechanism ensures that the estimation process remains reliable under varying temperature conditions by automatically adapting to the current thermal state of the battery, maintaining high reliability without requiring complex manual intervention.
3Measurement precision
If temperature compensation is implemented through variable gain scheduling, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent introduces dynamics by implementing a variable gain factor that changes with battery temperature in the extended Kalman filter. This dynamic adjustment improves measurement precision by adapting to thermal conditions while maintaining a relatively simple system structure, as the complexity is confined to the gain selection logic rather than the overall system architecture.
Solution Approach 2:
The patent modifies the gain factor parameter based on temperature to enhance measurement precision. By using predetermined gain factors selected according to temperature ranges, the system achieves temperature-compensated estimation accuracy without requiring complex additional hardware or fundamental changes to the filter structure, thus limiting the increase in device complexity.
4Reliability
If temperature compensation is implemented through variable gain scheduling, then the reliability is improved, but the ease of operation decreases
Solution Approach 1:
The patent uses feedback from battery temperature measurements to automatically adjust the gain factor in the extended Kalman filter. This feedback-driven approach improves reliability by ensuring accurate parameter estimation under varying temperature conditions while maintaining automated operation, reducing the need for manual intervention despite the added complexity of temperature-based gain scheduling.
Data Source
AI summary
A vehicle is provided with a vehicle system having an electric machine and a battery. The electric machine is configured to provide drive torque and the battery supplies power to the electric machine. The vehicle also includes a controller that is configured to generate output indicative of at least one of a battery power capability and a battery state of charge using a filter having a variable EKF gain factor based on battery temperature.